BACKGROUND AND PURPOSE: The ABCD system was developed to predict early stroke risk after transient ischemic attack. Incorporation of brain imaging findings has been suggested, but reports have used inconsistent methods and been underpowered. We therefore performed an international, multicenter collaborative study of the prognostic performance of the ABCD(2) score and brain infarction on imaging to determine the optimal weighting of infarction in the score (ABCD(2)I). METHODS: Twelve centers provided unpublished data on ABCD(2) scores, presence of brain infarction on either diffusion-weighted imaging or CT, and follow-up in cohorts of patients with transient ischemic attack diagnosed by World Health Organization criteria. Optimal weighting of infarction in the ABCD(2)I score was determined using area under the receiver operating characteristic curve analyses and random effects meta-analysis. RESULTS: Among 4574 patients with TIA, acute infarction was present in 884 (27.6%) of 3206 imaged with diffusion-weighted imaging and new or old infarction was present in 327 (23.9%) of 1368 imaged with CT. ABCD(2) score and presence of infarction on diffusion-weighted imaging or CT were both independently predictive of stroke (n=145) at 7 days (after adjustment for ABCD(2) score, OR for infarction=6.2, 95% CI=4.2 to 9.0, overall; 14.9, 7.4 to 30.2, for diffusion-weighted imaging; 4.2, 2.6 to 6.9, for CT; all P<0.001). Incorporation of infarction in the ABCD(2)I score improved predictive power with an optimal weighting of 3 points for infarction on CT or diffusion-weighted imaging. Pooled areas under the curve increased from 0.66 (0.53 to 0.78) for the ABCD(2) score to 0.78 (0.72 to 0.85) for the ABCD(2)I score. CONCLUSIONS: In secondary care, incorporation of brain infarction into the ABCD system (ABCD(2)I score) improves prediction of stroke in the acute phase after transient ischemic attack.
BACKGROUND AND PURPOSE: The ABCD system was developed to predict early stroke risk after transient ischemic attack. Incorporation of brain imaging findings has been suggested, but reports have used inconsistent methods and been underpowered. We therefore performed an international, multicenter collaborative study of the prognostic performance of the ABCD(2) score and brain infarction on imaging to determine the optimal weighting of infarction in the score (ABCD(2)I). METHODS: Twelve centers provided unpublished data on ABCD(2) scores, presence of brain infarction on either diffusion-weighted imaging or CT, and follow-up in cohorts of patients with transient ischemic attack diagnosed by World Health Organization criteria. Optimal weighting of infarction in the ABCD(2)I score was determined using area under the receiver operating characteristic curve analyses and random effects meta-analysis. RESULTS: Among 4574 patients with TIA, acute infarction was present in 884 (27.6%) of 3206 imaged with diffusion-weighted imaging and new or old infarction was present in 327 (23.9%) of 1368 imaged with CT. ABCD(2) score and presence of infarction on diffusion-weighted imaging or CT were both independently predictive of stroke (n=145) at 7 days (after adjustment for ABCD(2) score, OR for infarction=6.2, 95% CI=4.2 to 9.0, overall; 14.9, 7.4 to 30.2, for diffusion-weighted imaging; 4.2, 2.6 to 6.9, for CT; all P<0.001). Incorporation of infarction in the ABCD(2)I score improved predictive power with an optimal weighting of 3 points for infarction on CT or diffusion-weighted imaging. Pooled areas under the curve increased from 0.66 (0.53 to 0.78) for the ABCD(2) score to 0.78 (0.72 to 0.85) for the ABCD(2)I score. CONCLUSIONS: In secondary care, incorporation of brain infarction into the ABCD system (ABCD(2)I score) improves prediction of stroke in the acute phase after transient ischemic attack.
Authors: M F Giles; G W Albers; P Amarenco; E M Arsava; A W Asimos; H Ay; D Calvet; S B Coutts; B L Cucchiara; A M Demchuk; S C Johnston; P J Kelly; A S Kim; J Labreuche; P C Lavallee; J-L Mas; A Merwick; J M Olivot; F Purroy; W D Rosamond; R Sciolla; P M Rothwell Journal: Neurology Date: 2011-08-24 Impact factor: 9.910
Authors: Shadi Yaghi; Sara K Rostanski; Amelia K Boehme; Sheryl Martin-Schild; Alyana Samai; Brian Silver; Christina A Blum; Mahesh V Jayaraman; Matthew S Siket; Muhib Khan; Karen L Furie; Mitchell S V Elkind; Randolph S Marshall; Joshua Z Willey Journal: JAMA Neurol Date: 2016-05-01 Impact factor: 18.302